1. Field of the Invention
The present invention relates to a medical image processing apparatus and a medical image processing system which detect a candidate area of abnormal shadow by analyzing a medical image.
2. Description of Related Art
In a medical field, at the time of diagnosis, a doctor interprets a medical image such as an X-ray image, an ultrasound image or the like to find a lesion part and observes the course of disease state. Conventionally, for the purpose of reducing burdens on doctor's interpretation, what is developed is a Computer Aided Diagnosis (hereinafter, it is called CAD) apparatus which automatically detects shadow of a lesion part as an abnormal shadow candidate by image-analyzing image data of a medical image (see JP-Tokukai-2002-112986A).
Among the above-mentioned CAD apparatuses, there are ones to which an algorithm for detecting an abnormal shadow candidate by using sample data which is called training data is applied. In such an algorithm, training data which is previously classified according to a predetermined category such as an abnormal case, a normal case and the like is registered, and according to a judging logic which judges to which category judgment target data inputted based on this training data belongs, the detection of an abnormal shadow candidate is performed. As a judging logic using training data, a discriminant analysis method using the Mahalanobis distance, a method with support vector machine, a method with artificial neural network or the like is used.
The detection of an abnormal shadow candidate in general takes two steps. First, a medical image is analyzed and image feature (hereinafter, it is simply called feature) is calculated, and an area estimated as abnormal shadow is first-detected as an abnormal shadow candidate based on the feature. Next, the feature of the first-detected abnormal shadow candidate is inputted to a judging logic as judgment target data, and a category of the judgment target data is judged based on training data which is previously registered by the judging logic, and a second detection is performed. Then, only the judgment target data which belongs to a category to be outputted as a detection result of an abnormal shadow candidate is outputted as a conclusive detection result.
Since general training data which is previously prepared at manufacturer's side is used as the data to be used in the judging logic, a detection result of an abnormal shadow candidate according to the above-mentioned CAD is constant under the same detecting condition regardless of a doctor who performs the interpretation. However, depending upon a doctor who performs the interpretation, there are some requests such as the request that shadow which is obviously identified as abnormal shadow should not be detected by CAD, the request that specialty of CAD should be enhanced by increasing training data of a case in a field on which a doctor specializes, and the like.